Date of Award

Spring 5-2015

Degree Type


Degree Name


Degree Program

Computer Science


Computer Science

Major Professor

Hoque, Md Tamjidul


Energy functions are found to be a key of protein structure prediction. In this work, we propose a novel 3-dimensional energy function based on hydrophobic-hydrophilic properties of amino acid where we consider at least three different possible interaction of amino acid in a 3-dimensional sphere categorized as hydrophilic versus hydrophilic, hydrophobic versus hydrophobic and hydrophobic versus hydrophilic. Each of these interactions are governed by a 3-dimensional parameter alpha used to model the interaction and 3-dimensional parameter beta used to model weight of contribution. We use Genetic Algorithm (GA) to optimize the value of alpha, beta and Z-score. We obtain three energy scores libraries from a database of 4332 protein structures obtained from Protein Data Bank (PDB) server. Proposed energy function is found to outperform nearest competitor by 40.9% for the most challenging Rosetta decoy as well as better in terms of the Z-score based on Moulder and Rosetta decoy sets.


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